Eligibility: Graduate/postgraduate in chemistry/life sciences

Description/outline:

This course is ideal for students, researchers, and professionals in pharmaceuticals, biotechnology, and related fields looking to enhance their knowledge and skills in the modern drug discovery process. The course is divided into the following components:

In Silico Drug Design: Understanding molecular interactions, target identification, and the basics of drug-likeness.

Computational Techniques: Hands-on training in molecular docking, virtual screening, QSAR modeling, and molecular dynamics simulations.

Data Analysis: Use of bioinformatics and cheminformatics tools to analyze biological data and predict potential drug candidates.

Real-World Applications: Case studies highlighting successful computational strategies in drug discovery.

Computational Pharmacology: It involves the use of algorithms, simulations, and models to understand the interactions between drugs and biological systems, predict the behavior of drugs in the body, and optimize the design of new therapeutic agents.

Cheminformatics in Drug Discovery: Cheminformatics tools are used to create and manage large databases of chemical compounds, which include information on their structures, properties, and biological activities. 

Learner outcome: Learners will gain a comprehensive understanding of the drug discovery pipeline and the role of computational methods in accelerating this process. Learners will acquire skills in structure-based and ligand-based drug design, including pharmacophore modeling and ADMET prediction. They will be able to analyze and interpret data from computational experiments, applying this knowledge to identify and optimize potential drug candidates. Additionally, learners will understand the ethical and regulatory considerations in drug discovery. Through practical projects and case studies, they will develop critical thinking and problem-solving skills, preparing them for careers in pharmaceutical research and development.